Project Summary. Candidate. I am a pediatric nephrologist at Yale University dedicated to improving outcomes for children with chronic kidney disease (CKD). The goal of my application is to obtain mentored research training to develop a biomarker-augmented risk prediction model of pediatric CKD progression for use in future clinical trials. This research will build upon my prior training, which focused on the risk of CKD after acute kidney injury and the use of biomarkers to predict renal outcomes in children. This proposal will provide me with hands-on learning and formal didactic coursework in advanced statistics, biomarker methodology, and pediatric CKD. I will also intensely focus on developing the professional skills necessary for establishing effective collaborations, scientific writing, and obtaining funding to support my research. To accomplish my stated plan, I have the support of my highly qualified primary co-mentors (Drs. Chirag Parikh and Susan Furth) and mentoring committee (Drs. Haiqun Lin, Eugene Shapiro, and Prasad Devarajan) with interdisciplinary expertise in the fields of kidney injury biomarkers, translational research, biostatistics, and pediatric CKD. This multidisciplinary mentorship along with the highly skilled training environment at Dr. Parikh's, Program of Applied Translational Research will allow me to conduct my proposed research and establish an independently funded research program. Project. Progression of CKD in children leads to end stage renal disease (ESRD), which is associated with mortality rates 30-150 times higher than the general pediatric population. The traditional biomarkers, serum creatinine and proteinuria, are used to predict progression of CKD in clinical trials even though both correlate poorly with the progression of CKD and the response to interventions. There are numerous candidate therapies for CKD, but with a continued reliance on serum creatinine and proteinuria, clinical trials will likely continue to fail. The field of CKD biomarkers in children is a very promising area of research with a small amount of resources invested to date. Predicting progression of CKD will allow clinicians to better time follow-up, referral for transplant, and provide better guidance to families. More importantly, an optimal panel of biomarkers and risk prediction model can replace proteinuria and serum creatinine in biomarker guided clinical trials. We plan to measure urine and serum biomarkers of kidney injury, inflammation, repair, and fibrosis from the baseline samples of the 869 children with CKD enrolled in the CKD in Children (CKiD) cohort and determine their relationship with longitudinal measured GFR decline and incident ESRD. The optimal combination of biomarkers plus clinical variables from 2/3rd's of the CKiD patients will yield a risk prediction model to predict CKD progression. Our risk prediction model will be validated for longitudinal GFR decline, internally in 1/3rd of the CKiD patients, and externally in the 124 children of the Assessment, Serial Evaluation, and Subsequent Sequelae in AKI cohort. Developing a risk prediction model of CKD progression can be paradigm changing, transforming clinical care for children with CKD.